Red Hat OpenShift is part of the Cloud Native Computing Foundation (CNCF) Certified Program, ensuring portability and interoperability for your container workloads. This also allows you to use Kubernetes tools to interact with an OpenShift cluster, like
kubectl, and you can rest assured that all the APIs you know and love are right there at your fingertips.
The Kubernetes Python client is another great tool for interacting with an OpenShift cluster, allowing you to perform actions on Kubernetes resources with Python code. It also has applications within a cluster. We can configure a Python application running on OpenShift to consume the OpenShift API, and list and create resources. We could then create containerized batch jobs from the running application, or a custom service monitor, for example. It sounds a bit like “OpenShift inception,” using the OpenShift API from services created using the OpenShift API.
In this article, we’ll create a Flask application running on OpenShift. This application will use the Kubernetes Python client to interact with the OpenShift API, list other pods in the project, and display them back to the user.
Continue reading “Use the Kubernetes Python client from your running Red Hat OpenShift pods”
Red Hat CodeReady Workspaces provide developers with containerized development environments hosted on OpenShift/Kubernetes. DevOps teams can now use a hosted development environment that’s pre-built for their chosen stack and customized for their project.
CodeReady Workspaces can help you rapidly onboard developers for your project as everything they need to develop is running in a containerized workspace. In this post, we’re going to use CodeReady Workspaces to get up and running quickly with an existing open source project, Peak. Peak is a multi-container Kubernetes application for performance testing web services, and it allows you to create distributed performance tests using the Kubernetes Batch API for test orchestration. We’ll make some modifications to Peak’s Flask front end, a stateless web interface that interacts with a Falcon RESTful API to return data about performance tests. You won’t need the complete Peak application deployed, though if you like, you can find steps to deploy it to OpenShift here.
To follow along you’ll need a Red Hat OpenShift Container Platform 3.11 environment. You can use the Red Hat Container Development Kit on your Windows, macOS, or Linux laptop or a hosted Red Hat OpenShift instance to do it on online.
Continue reading “Creating a containerized Python/Flask development environment with Red Hat CodeReady Workspaces”
TL;DR Of course we have Python! You just need to specify if you want Python 3 or 2 as we didn’t want to set a default. Give
yum install python3 and/or
yum install python2 a try. Or, if you want to see what we recommend you install
yum install @python36 or
yum install @python27. Read on for why:
For prior versions of Red Hat Enterprise Linux, and most Linux Distributions, users have been locked to the system version of Python unless they got away from the system’s package manager. While this can be true for a lot of tools (ruby, node, Perl, php) the Python use case is more complicated because so many Linux tools (like yum) rely on Python. In order to improve the experience for RHEL 8 users, we have moved the Python used by the system “off to the side” and we introduced the concept of Application Streams based on Modularity.
Continue reading “What, No Python in RHEL 8 Beta?”
Ten years ago, the developers of the Python programming language decided to clean things up and release a backwards-incompatible version, Python 3. They initially underestimated the impact of the changes, and the popularity of the language. Still, in the last decade, the vast majority of community projects has migrated to the new version, and major projects are now dropping support for Python 2.
In Red Hat Enterprise Linux 8, Python 3.6 is the default. But Python 2 remains available in RHEL 8.
Using Python in RHEL 8
To install Python, type
yum install python3.
To run Python, type
If that doesn’t work for you, or you need more details, read on!
Continue reading “Python in RHEL 8”
This article shows how to install Python 3,
pipenv on Red Hat Enterprise Linux 7. After following the steps in this article, you should be in a good position to follow many Python guides and tutorials using RHEL. Note: For RHEL 8 installs, See Python on RHEL 8.
Using Python virtual environments is a best practice to isolate project-specific dependencies and create reproducible environments. Other tips and FAQs for working with Python and software collections on RHEL 7 are also covered.
There are a number of different ways to get Python 3 installed on RHEL. This article uses Red Hat Software Collections because these give you a current Python installation that is built and supported by Red Hat. During development, support might not seem that important to you. However, support is important to those who have to deploy and operate the applications you write. To understand why this is important, consider what happens when your application is in production and a critical security vulnerability in a core library (for example SSL/TLS) is discovered. This type of scenario is why many enterprises use Red Hat.
Python 3.6 is used in this article. It was the most recent, stable release when this was written. However, you should be able to use these instructions for any of the versions of Python in Red Hat Software Collections including 2.7, 3.4, 3.5, and future collections such as 3.7.
Continue reading “How to install Python 3 on Red Hat Enterprise Linux”
In this article, we will use a Python-based messaging client to connect and subscribe to a topic with a durable subscription in the Apache ActiveMQ Artemis broker. We will use the text-based STOMP protocol to connect and subscribe to the broker. STOMP clients can communicate with any STOMP message broker to provide messaging interoperability among many languages, platforms, and brokers.
If you need to brush up on the difference between persistence and durability in messaging, check Mary Cochran’s article on developers.redhat.com/blog.
A similar process can be used with Red Hat AMQ 7. The broker in Red Hat AMQ 7 is based on the Apache ActiveMQ Artemis project. See the overview on developers.redhat.com for more information.
Continue reading “Using the STOMP Protocol with Apache ActiveMQ Artemis Broker”
I recently got my zero-dollar developer copy of Red Hat Enterprise Linux (RHEL, version 7.5) and built a virtual machine (VM) to run it. There it was, on my PC, running in VirtualBox…a gleaming, shiny, brand-spanking-new VM running RHEL. Whatever shall I do with it?
Then I got the idea: I’ll install the Red Hat Container Development Kit (CDK) and build some Python-based containers. I’ll use Flask, a terrific microframework that makes building RESTful services easy.
Continue reading “How to install Python Flask on Red Hat Enterprise Linux 7”
There has been a need for a simple, easy-to-use handler for writing tests and other code around containers that would implement helpful methods and utilities. For this we introduce conu, a low-level Python library.
This project has been driven from the start by the requirements of container maintainers and testers. In addition to basic image and container management methods, it provides other often used functions, such as container mount, shortcut methods for getting an IP address, exposed ports, logs, name, image extending using source-to-image, and many others.
Continue reading “Introducing conu – Scripting Containers Made Easier”
APIs are critical to automation, integration and developing cloud-native applications, and it’s vital they can be scaled to meet the demands of your user-base. In this article, we’ll create a database-backed REST API based on the Python Falcon framework using Red Hat Software Collections (RHSCL), test how it performs, and scale-out in response to a growing user-base.
Continue reading Create a scalable REST API with Falcon and RHSCL
What is Rust?
Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.
- zero-cost abstractions
- move semantics
- guaranteed memory safety
- threads without data races
- trait-based generics
- pattern matching
- type inference
- minimal runtime
- efficient C bindings
Continue reading “Speed up your Python using Rust”